Version 2.7.0

Version 2.7.0

• CLTune now automatically ensures global size is a multiple of the local workgroup size
• Added GetBestResult() to the tuner's API to retrieve the best parameters programmatically
• Changed std::initalizer_list in the AddParameters API to std::vector
• Fixed a bug in the simulated annealing search method

Version 2.6.0

Version 2.6.0

• Changed timing measurements to now also include the (varying) kernel launch overhead
• It is now possible to set OpenCL compiler options through the env variable CLTUNE_BUILD_OPTIONS
• Added support for compilation under Visual Studio 2013 (MSVC++ 12.0)
• Added an option to build a static version of the library

Version 2.5.0

Version 2.5.0

• Updated to version 8.0 of the CLCudaAPI header
• Made it possible to configure the number of times each kernel is run (to average results)
• Minor bugfixes

Version 2.4.0

Version 2.4.0

• Made it possible to run the unit-tests independently of the provided OpenCL kernel samples
• Added an option to compile in verbose mode for additional diagnostic messages (-DVERBOSE=ON)
• Now using version 6.0 of the CLCudaAPI header
• Fixed the RPATH settings on OSX
• Added Appveyor continuous integration and increased coverage of the Travis builds

Version 2.3.1

Version 2.3.1 (bug-fix release)

• Fixed a bug where an output buffer could not be used as input at the same time
• Fixed computing the validation error for half-precision fp16 data-types

Version 2.3.0

Version 2.3.0

• Added support for 'short' and 'cl_half' data-types as kernel buffer and scalar arguments
• Fixed a bug where failed results would still show up in the tuning results
• Made MSVC link the run-time libraries statically

Version 2.2.0

Version 2.2.0

• Added two new simpler samples of using the tuner (vector-add and convolution)
• Updated the general documentation
• Added API documentation
• Now using version 5.0 of the CLCudaAPI header

Version 2.1.0

Version 2.1.0

• Added exports to be able to create a DLL on Windows (thanks to Marco Hutter)
• Added command-line OpenCL platform selection in the examples (thanks to William J Shipman)

Version 2.0.0

Version 2.0.0

• Added support for machine learning models. These models can be trained on a small fraction of the
  tuning configurations and can be used to predict the remainder. Two models are supported:
  • Linear regression
  • A 3-layer neural network
• Now using version 4.0 of the CLCudaAPI header (previously known as Claduc)
• Added experimental support for CUDA kernels
• Added support for MSVC (Visual Studio) 2015
• Using Catch instead of GTest for unit-testing
• Various minor fixes

Version 1.7.0

Version 1.7.0

• Now using the Claduc C++11 interface to OpenCL (see https://github.com/CNugteren/Claduc)
• Added a method to print all tuning results in JSON-format to file